Row selection: loc[] vs iloc[]
A big part of working with DataFrames is to locate specific entries in the dataset. You can locate rows in two ways:
- By a specific value of a column (feature).
- By the index of the rows (index). In this exercise, we will focus on the second way.
If you have previous experience with pandas, you should be familiar with the .loc and .iloc indexers, which stands for 'location' and 'index location' respectively. In most cases, the indices will be the same as the position of each row in the Dataframe (e.g. the row with index 13 will be the 14th entry).
While we can use both functions to perform the same task, we are interested in which is the most efficient in terms of speed.
This exercise is part of the course
Writing Efficient Code with pandas
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Define the range of rows to select: row_nums
row_nums = range(0, 1000)
# Select the rows using .loc[] and row_nums and record the time before and after
loc_start_time = time.time()
rows = poker_hands.____[____]
loc_end_time = ___
# Print the time it took to select the rows using .loc[]
print("Time using .loc[]: {} sec".format(___ - ___))